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Social Network Analysis and Mining
Published in:

01-12-2020 | Original Article

On knowledge-transfer characterization in dynamic attributed networks

Authors: Thiago H. P. Silva, Alberto H. F. Laender, Pedro O. S. Vaz de Melo

Published in: Social Network Analysis and Mining | Issue 1/2020

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Abstract

How do social aspects influence knowledge transfer in dynamic attributed networks? We address this issue by characterizing the behavior of the actors and their dynamic interactions based on the strategic positioning in a social structure. For this, we propose a method to characterize the behavior of nodes and their dynamic relationships based on temporal node attributes that capture how knowledge is transferred across a network. In order to assess our method, we apply it to unveil the differences of social relationships in distinct academic social networks and Q&A communities. We also validate our social definitions considering the importance of the nodes and edges in a social structure by means of network properties, as well as investigate the robustness of our method by stressing it for dealing with the time existence of the nodes in a network and the randomness of attributes associated with them. Moreover, we propose an unsupervised method to measure the academic importance of researchers based on our knowledge-transfer model, which outperforms traditional network metrics and other social-based approaches.
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Footnotes
1
Note that the sets \((\varGamma _1(u), \varGamma _{1+k}(u), \ldots , \varGamma _t(u))\) are dynamically built according to the degree of persistence, i.e., different instants k may contain completely distinct sets of relevant attributes.
 
2
Association for Computing Machinery: http://​www.​acm.​org/​sigs.
 
3
Data collected from DBLP (https://​dblp.​uni-trier.​de) in June, 2018.
 
4
Stack Exchange: https://​stackexchange.​com, data collected in September, 2018.
 
5
We tested other values, but the results and conclusions are similar to the ones reported in this work.
 
6
Here, we omit the comparative results of our method with those of the RECAST social classification strategy (Vaz de Melo et al. 2015) and of our previously proposed social-based method (Silva et al. 2018), which were discussed by Silva et al. (2019). Overall, although based on different social perspectives, they are coherent regarding the edges’ strength.
 
7
As the distribution values per class did not pass the normality test, we evaluated the statistical significance between each two classes by means of the nonparametric Mann–Whitney–Wilcoxon test and among all classes by means of its extension given by the Kruskal–Wallis test (Hollander et al. 2013). All experiments were performed with a significance level of \(\alpha = 0.05\).
 
8
In our previous work (Silva et al. 2019), we analyzed the betweenness centrality distributions for RECAST (Vaz de Melo et al. 2015) and our social-based method (Silva et al. 2018). In summary, we highlighted that RECAST fails to separate social from casual relationships, and although our social-based method showed some coherence between its social definition and the network properties, it does not statistically distinguish its classes as bridges.
 
9
We also tested an alternative formulation for Eq. 1 that considered the closure expressed by means of the clustering coefficient centrality and the brokerage expressed by the betweenness centrality, but it achieved inferior results.
 
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Metadata
Title
On knowledge-transfer characterization in dynamic attributed networks
Authors
Thiago H. P. Silva
Alberto H. F. Laender
Pedro O. S. Vaz de Melo
Publication date
01-12-2020
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2020
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-020-00657-4

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